Advancements in deep learning have resulted in significant progress in the field of machine vision. One common challenge in training deep learning networks is the amount of quality training data that is required to train a model. The successful training of a machine vision system is dependent on having sufficient data. Most current methods for gathering training data require capture of real-world images with manual annotation of the visual data.
An alternative to the manual method for training machine vision system is to use computer-generated data, also known as synthetic data. Computer algorithms can be used to simulate real-world images and generate accurate annotations of the visual data. This reduces the dependence on manual methods of data collection and allows for scalable generation of training data. Synthetic data can be used alone or used in conjunction with real-world data to train machine vision systems. In addition, the collection of data is not a straightforward task. There are many challenges in determining what data to collect in terms of subject, quality, and distribution of the collected images. Acquiring appropriate data entails gathering or identifying the data that correlates with the desired outcomes.